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--- |
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license: llama2 |
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base_model: codellama/CodeLlama-7b-Instruct-hf |
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language: |
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- en |
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tags: |
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- arxiv:2406.11717 |
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--- |
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# codellama-abliterated-2xd |
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CodeLlama-7b-Instruct-hf adapted using the abliteration notebook from [Maxime Labonne's LLM Course](https://github.com/mlabonne/llm-course) |
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Based on the paper ["Refusal in Language Models Is Mediated by a Single Direction"](https://arxiv.org/abs/2406.11717) |
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**This version 2x-d the intervention vector**; see code model with less intervention: https://huggingface.co/monsoon-nlp/codellama-abliterated |
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**Based on CodeLlama/Llama2 and subject to the restrictions of that model and license - not for unapproved uses**: |
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## Concept |
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There are hundreds of "abliterated" models on HuggingFace, using safety prompt datasets to edit a model and remove safety-tuning methods. |
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None of these abliterated models have explored code LLMs, code-generation, and CyberSecEval. I don't know a lot about how well these will |
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work, but this is a first step. |
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Blog: https://huggingface.co/blog/monsoon-nlp/refusal-in-code-llms |
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## Usage |
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```python |
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! pip install transformers accelerate --quiet |
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig |
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tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-Instruct-hf") |
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model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/codellama-abliterated-2xd", device_map="auto") |
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code_generator = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False) |
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input_string = "[INST] Write a python function to calculate the factorial of a number [/INST]" |
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generated_code = code_generator(input_string, max_length=100)[0]['generated_text'] |
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print(generated_code) |
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``` |
